code: def reset(
      self,
      *,
      seed: Optional[int] = None,
      options: Optional[dict] = None,
  ) -> Tuple[MultiAgentDict, MultiAgentDict]:
      # Call the super's reset function.
      super().reset(seed=seed, options=options)
  
      # Set the timestep back to zero.
      self.t = 0
      # The number of agents that are ready at this timestep.
      # Note, if we want to use an RNG, we need to use the one from the
      # `gym.Env` otherwise results are not reproducable. This RNG is
      # stored to `self._np_random`.
      num_agents_step = self._np_random.integers(
          1, len(self._agent_ids)
      )
      # The agents that are ready.
      agents_step = self._np_random.choice(
          np.array(sorted(self._agent_ids)),
          num_agents_step,
          replace=False,
      )
      # Initialize observations.
      init_obs = {agent_id: 0 for agent_id in agents_step}
      init_info = {agent_id: {} for agent_id in agents_step}
  
      # Reset all alive agents to all agents.
      self._agents_alive = set(self._agent_ids)
  
      return init_obs, init_info
  
imports: import gymnasium
  import numpy
  import unittest
  from typing import List, Optional, Tuple
  import ray
  from ray.rllib.env.multi_agent_env import MultiAgentEnv
  from ray.rllib.env.multi_agent_episode import MultiAgentEpisode
  from ray.rllib.utils.typing import MultiAgentDict
label_desc: Use image node_id 2 for calling the MultiAgentTestEnv obj's underlying member method code with example usage: obj.reset() and returns: init_obs,  init_info